Visible light communication (VLC) refers to the communication of information by means of a signal embedded in visible light, sometimes also referred to as coded light. The information is embedded by modulating a property of the visible light according to any suitable modulation technique. E.g. according to one example of a coded light scheme, the intensity of the visible light from each of multiple light sources is modulated to form a carrier waveform having a certain modulation frequency, with the modulation frequency being fixed for a given one of the light sources but different for different ones of the light sources such that the modulation frequency acts as a respective identifier (ID) of each light source. In more complex schemes a property of the carrier waveform may be modulated in order to embed symbols of data in the light emitted by a given light source, e.g. by modulating the amplitude, frequency, phase or shape of the carrier waveform in order to represent the symbols of data. In yet further possibilities, a baseband modulation may be used i.e. there is no carrier wave, but rather symbols are modulated into the light as patterns of variations in the brightness of the emitted light. This may either be done directly (intensity modulation) or indirectly (e.g. by modulating the mark:space ratio of a PWM dimming waveform, or by modulating the pulse position).
The current adoption of LED technology in the field of lighting has brought an increased interest in the use of coded light to embed signals into the illumination emitted by luminaires, e.g. room lighting, thus allowing the illumination from the luminaires to double as a carrier of information. Preferably the modulation is performed at a high enough frequency and low enough modulation depth to be imperceptible to human vision, or at least such that any visible temporal light artefacts (e.g. flicker and/or strobe effects) are weak enough to be tolerable to humans.
Based on the modulations, the information in the coded light can be detected using a photodetector. This can be either a dedicated photocell, or a camera comprising an array of photocells (pixels) and a lens for forming an image on the array. E.g. the camera may be a general purpose camera of a mobile user device such as a smartphone or tablet. Camera based detection of coded light is possible with either a global-shutter camera or a rolling-shutter camera (e.g. rolling-shutter readout is typical to mobile CMOS image sensors found in mobile devices such as smartphones and tablets). In a global-shutter camera the entire pixel array (entire frame) is captured at the same time, and hence a global shutter camera captures only one temporal sample of the light from a given luminaire per frame. In a rolling-shutter camera on the other hand, the frame is divided into lines (typically horizontal rows) and the frame is exposed line-by-line in a temporal sequence, each line in the sequence being exposed at a slightly later time than the last. Thus the rolling-shutter readout causes fast temporal light modulations to translate into spatial patterns in the line-readout direction of the sensor, from which the encoded signal can be decoded. Hence while rolling-shutter cameras are generally the cheaper variety and considered inferior for purposes such as photography, for the purpose of detecting coded light they have the advantage of capturing more temporal samples per frame, and therefore a higher sample rate for a given frame rate. Nonetheless coded light detection can be achieved using either a global-shutter or rolling-shutter camera as long as the sample rate is high enough compared to the modulation frequency or data rate (i.e. high enough to detect the modulations that encode the information).
Coded light has many possible applications. For instance a different respective ID can be embedded into the illumination emitted by each of the luminaires in a given environment, e.g. those in a given building, such that each ID is unique at least within the environment in question. E.g. the unique ID may take the form of a unique modulation frequency or unique sequence of symbols. This can then enable any one or more of a variety of applications. For example if a mobile device for remotely controlling the luminaires is equipped with a light sensor such as a camera, then the user can direct the sensor toward a particular luminaire or subgroup of luminaires so that the mobile device can detect the respective ID(s) from the emitted illumination captured by the sensor, and then use the detected ID(s) to identify the corresponding one or more luminaires in order to control them. This provides a user-friendly way for the user to identify which luminaire or luminaires he or she wishes to control. E.g. the mobile device may take the form of a smartphone or tablet running a lighting control app, with the app being configured to detect the embedded IDs from the captured light and enact the corresponding control functionality.
As another example, there may be provided a location database which maps the ID of each luminaire to its location (e.g. coordinates on a floorplan), and this database may be made available to mobile devices from a server via one or more networks such as the Internet and/or a wireless local area network (WLAN). Then if a mobile device captures an image or images containing the light from one or more of the luminaires, it can detect their IDs and use these to look up their locations in the location database in order to detect the location of the mobile device based thereon. E.g. this may be achieved by measuring a property of the received light such as received signal strength, time of flight and/or angle of arrival, and then applying technique such as triangulation, trilateration, multilateration or fingerprinting, or simply by assuming that the location of the nearest or only captured luminaire is approximately that of the mobile device (and in some cases such information may be combined with information from other sources, e.g. on-board accelerometers, magnetometers or the like, in order to provide a more robust result). The detected location may then be output to the user through the mobile device for the purpose of navigation, e.g. showing the position of the user on a floorplan of the building. Alternatively or additionally, the determined location may be used as a condition for the user to access a location based service. E.g. the ability of the user to use his or her mobile device to control the lighting (or another utility such as heating) in a certain region (e.g. a certain room) may be made conditional on the location of his or her mobile device detected to be within that same region (e.g. the same room), or perhaps within a certain control zone associated with the lighting in question. Other forms of location-based service may include, e.g., the ability to make or accept location-dependent payments.
As another example, a database may map luminaire IDs to location specific information such as information on a particular museum exhibit in the same room as a respective one or more luminaires, or an advertisement to be provided to mobile devices at a certain location illuminated by a respective one or more luminaires. The mobile device can then detect the ID from the illumination and use this to look up the location specific information in the database, e.g. in order to display this to the user of the mobile device. In further examples, data content other than IDs can be encoded directly into the illumination so that it can be communicated to the receiving device without requiring the receiving device to perform a look-up.
Thus the use of a camera to detect coded light has various commercial applications in the home, office or elsewhere, such as a personalized lighting control, indoor navigation, location based services, etc.
Typically for such applications the so-called front-facing camera of the smartphone is used (the camera on the same face as the device's main screen, typically a touchscreen). Thus the camera directly captures the luminaires on the ceiling above the user while also keeping the device's screen suitably orientated to be viewed by the user. FIGS. 2a and 2b show an example of a lighting system composed of adjacent luminaires in the form of ceiling tiles. FIG. 2a shows the humanly visible appearance to the human user the fast modulation of the coded light is imperceptible and the light intensity appears constant. FIG. 2b on the other hand shows the appearance as captured by a rolling shutter camera under short exposure capture (with the dashed line indicating the rolling-shutter readout direction). Here the coded light modulation appears as spatial patterns in each of the luminaires, each of which associated with a different specific code, e.g. different respective ID. In the example shown the capture is by a rolling-shutter camera such that the message from each luminaires appears as a different spatial pattern in the captured image. However it will be appreciated that capture with a global-shutter camera is also possible, in which case the modulation is a captured as a temporal modulation over multiple frames (and in fact with a rolling-shutter camera, in some cases the pattern from multiple frames may be stitched together).
In other forms of wireless data communication, ‘channel separability’ has to be implemented by mathematical signal orthogonality, e.g. the use of sine waves of different frequency, or more generally frequency multiplexing; or else by the use of a transmission protocol, e.g. use of repeated transmission using randomized packet intervals (the so-called ALOHA protocol). But when multiple luminaires simultaneously fill the field of view of the camera, such that multiple luminaires emitting different signals are captured in the same frame, then image-based segmentation can be used to separate the different luminaires prior to decoding of the information embedded in the coded light. I.e. camera based detection of coded light has the advantage that when light is received simultaneously from multiple coded light sources, it is also received with spatial separation between the light from the different sources, because this light appears in different spatial regions of the image separated by a recognizable gap or division in between (e.g. see again FIG. 2a). The image-based segmentation essentially provides a form of channel separation among multiple signals that might be difficult or impossible to decode otherwise. Therefore, concurrent detection of multiple coded light sources does not have to rely on ‘channel separability’ as an inherent characteristic of the signals themselves.